Drivable region detection is a crucial upstream task for autonomous navigation, so speed and accuracy are the most critical indicators for safe driving. In this article, we proposed a novel representation paradigm for LiDAR data, whereby the drivable region can be efficiently detected and transformed into a dense region in the bird's eye view. Our method differs from the conventional spatial feature extraction and deep learning-based computation-intensive methods. Based on the proposed representation paradigm, our method takes full advantage of image-based features and processing to capture the boundaries between drivable and non-drivable regions within 10 ms solely on a CPU clocked at 4.0 GHz, thus suitable for most mobile platforms with various computational resources. Our contributions are fourfold. Firstly, we propose UpBEV, a representation addressing the sparsity of the point cloud from LiDAR. With this representation, the boundaries are projected into a 2D image and become distinguishable. Second, we develop a complete framework for road detection based on UpBEV, directly generating a dense top-view drivable region that is essential for navigation. Third, with comprehensive experiments on KITTI-Road dataset and SemanticKITTI dataset, the accuracy, speed, and robustness of our method are demonstrated well. Particularly, our method outperforms all the state-of-the-art non-learning methods on the KITTI-Road Benchmark in both maximum F1-measure and runtime, regardless of data type.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    UpBEV: Fast and Accurate LiDAR-Based Drivable Region Detection Utilizing Uniform Polar BEV


    Contributors:
    Wen, Hao (author) / Wang, Tianci (author) / Chen, Yong (author) / Liu, Chunhua (author)

    Published in:

    Publication date :

    2024-10-01


    Size :

    6248779 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Drivable Region Completion via a 3D LiDAR

    Jang, Wonje / Kim, Euntai | IEEE | 2024


    THERMAL-INFRARED BASED DRIVABLE REGION DETECTION

    Yoon, Jae Shin / Park, Kibaek / Hwang, Soonmin et al. | British Library Conference Proceedings | 2016


    PEDAL-DRIVABLE AND ELECTRICALLY-DRIVABLE VEHICLE

    BARMBY ADAM | European Patent Office | 2021

    Free access

    Drivable Space Characterization Using Automotive Lidar and Georeferenced Map Information

    Moras, J. / Rodriguez Florez, S.A. / Drevelle, V. et al. | British Library Conference Proceedings | 2012


    Off-Road Drivable Area Extraction Using 3D LiDAR Data

    Gao, Biao / Xu, Anran / Pan, Yancheng et al. | IEEE | 2019